code: def softmax(_outputs):
      maxes = np.max(_outputs, axis=-1, keepdims=True)
      shifted_exp = np.exp(_outputs - maxes)
      return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
  
file_path: transformers/src/transformers/pipelines/text_classification.py
imports: import inspect
  import warnings
  from typing import Dict
  import numpy
  from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
  from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
label_desc: Use image node_id 2 for calling a global function with example usage: softmax(_outputs) and returns: unknown